Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition

Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals

Towards New Strategies for Improving the Transfer of Innovation Between University and the Food Industry

All commerce relies on effective strategies for completing a deal, but conducting the transaction at the university-industry interface with an intangible asset represented by research results remains a difficult proposition. Beyond differences of mission and culture, it is usually assumed that the established language of technology transfer can permit productive communication by a university across a wide diversity of industries. However, the experience of the authors indicates that an appreciation of aspects such as subtleties of language, conflicting goals, and market understanding must also be brought to bear in successfully completing a transaction. Information asymmetry remains a key challenge to overcome in this task, and the example of the food industry represents a special case. This article reviews key developments in technology transfer of food innovation across the university-industry interface in Ireland and suggests possible new directions for exploration in order to improve the effectiveness of this process

Rethinking transfer: Learning from CALL teacher education as consequential transition

Behind CALL teacher education (CTE) there is an unproblematized consensus of transfer, which suggests a positivist and tool-centered view of learning gains that differs from the sociocultural focus of recent teacher education research. Drawing on Beach’s (2003) conceptualization of transfer as consequential transition, this qualitative study seeks a cross-contextual understanding of language teacher learning with digital technology as the teachers in this study moved from a CTE course back to their own teaching contexts. Near the end of a CTE course, 19 in-service language teachers were asked to build connections between their experiences in the course and their teaching by creating a presentation. Four types of connections were identified, including thoughtful action planning, past experience refinement, and limited and reluctant use. In-depth interviews eight months later with four of the teachers found that they could seldom use the tools in the ways they had planned. However, they each experienced consequential transition as they struggled to reflect on their CTE course experience in everyday teaching. These results challenge the view that transfer in CTE must be about using technology. It is suggested that a focus on critical reflection of technology use may encourage teachers to continue reflective engagement in the ever-changing and complicated digital learning and teaching context

Investigating and improving classroom activities to enhance Japanese language learning

The importance of foreign languages is evident particularly as nation building is highly dependent on the transfer of foreign technology. Japanese Language is considered to be a difficult foreign language to learn among the foreign languages because of its complicated writing system. Teaching the writing system within a constant time period is a challenge in the institute of higher education. In order to inform my teaching and to improvise my teaching of Hiragana, an action research has been carried out to explore how I made the participants learn Hiragana. Reflection on the lessons has been done critically on my teaching styles. Students learning style has been examined to inform the teaching style. Consequently, using Japanese input to enforce the reading and writing of Hiragana has been taken as an action to address the problem. Results showed that majority of the participants have demonstrated a significant improvement. However, there were a few students who were unable to improve their Hiragana ability. Reflection need to been done to address the problem in the next cycle

Style Transfer in Text: Exploration and Evaluation

Style transfer is an important problem in natural language processing (NLP). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics. In this paper, we propose to learn style transfer with non-parallel data. We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. We also propose novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation. We access our models and the evaluation metrics on two tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.Comment: To appear in AAAI-1

Demo of three ways to use a computer to assist in lab

The objective is to help the slow learner and students with a language problem, or to challenge the advanced student. Technology has advanced to the point where images generated on a computer can easily be recorded on a VCR and used as a video tutorial. This transfer can be as simple as pointing a video camera at the screen and recording the image. For more clarity and professional results, a board may be inserted into a computer which will convert the signals directly to the TV standard. Using a computer program that generates movies one can animate various principles which would normally be impossible to show or would require time-lapse photography. For example, you might show the change in shape of grains as a piece of metal is cold worked and then show the recrystallization and grain growth as heat is applied. More imaginative titles and graphics are also possible using this technique. Remedial help may also be offered via computer to those who find a specific concept difficult. A printout of specific data, details of the theory or equipment set-up can be offered. Programs are now available that will help as well as test the student in specific areas so that a Keller type approach can be used with each student to insure each knows the subject before going on to the next topic. A computer can serve as an information source and contain the microstructures, physical data and availability of each material tested in the lab. With this source present unknowns can be evaluated and various tests simulated to create a simple or complex case study lab assignment